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Accelerated catadioptric omnidirectional view image unwrapping processing using GPU parallelisation

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Abstract

Catadioptric omnidirectional view sensors have found increasing adoption in various robotic and surveillance applications due to their 360° field of view. However, the inherent distortion caused by the sensors prevents their direct utilisations using existing image processing techniques developed for perspective images. Therefore, a correction processing known as “unwrapping” is commonly performed. However, the unwrapping process incurs additional computational loads on central processing units. In this paper, a method to reduce this burden in the computation is investigated by exploiting the parallelism of graphical processing units (GPUs) based on the Compute Unified Device Architecture (CUDA). More specifically, we first introduce a general approach of parallelisation to the said process. Then, a series of adaptations to the CUDA platform is proposed to enable an optimised usage of the hardware platform. Finally, the performances of the unwrapping function were evaluated on a high-end and low-end GPU to demonstrate the effectiveness of the parallelisation approach.

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Notes

  1. The coalesced access of the unwrapping kernel can be confirmed using the NVIDIA visual profiler [17].

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Acknowledgments

Nguan Soon Chong thanks Swinburne University of Technology (Sarawak Campus) for his Ph.D. studentship. The authors would like to personally thank Jian Soon Ng and Albin Sui Hian Kuek for their useful discussion on this research topic.

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Authors and Affiliations

  1. Faculty of Engineering, Computing and Science, Swinburne University of Technology (Sarawak Campus), 93350, Kuching, Sarawak, Malaysia

    Nguan Soon Chong & M. L. Dennis Wong

  2. School of Engineering, Nazarbayev University, Astana, Kazakhstan

    Yau Hee Kho

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  1. Nguan Soon Chong
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  2. M. L. Dennis Wong
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  3. Yau Hee Kho
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Correspondence to Nguan Soon Chong.

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Chong, N.S., Wong, M.L.D. & Kho, Y.H. Accelerated catadioptric omnidirectional view image unwrapping processing using GPU parallelisation. J Real-Time Image Proc 12, 55–69 (2016). https://doi.org/10.1007/s11554-013-0390-x

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